The present invention relates in general to substrate manufacturing technologies and in particular to computer-implemented data presentation techniques for a plasma processing system.
Semiconductor fabrication facilities often cost billions of dollars to design and operate. Optimizing throughput and decreasing costs are therefore critical for profitability. Capital equipment processing systems within these facilities, however, often require significant human manual intervention creating the potential for process variances or even outright operation mistakes.
Most substrate capital equipment processing systems are normally controlled by sophisticated computers comprising operating software programs, wherein users via an interface are provided the ability to send requests to the equipment and receive output information from the equipment. Often thousands of process measurements (i.e., sequentially measured electromagnetic emission spectra during a target etch step) are periodically measured and subsequently transformed to a smaller set of aggregate variables that correlate to a system parameter of interest (i.e., such as whether a chuck must be replaced).
However, efficiently analyzing, summarizing, and displaying process information in a format that is both understandable and actionable to the operator, while the substrate is being processed, may be problematic. In general, the plasma process is dynamic and hence difficult to monitor. For example, residual may build up on the chamber walls changing the chemical properties of the plasma, or plasma damage may occur to chamber structures altering the electrical characteristics of the process, etc.
Plasma processing equipment is commonly configured with some type of discrete visual notification or alarm device that notifies the operator as to whether the system is ready for operation. A basic, although common, type of notification system comprises a series of lights coupled to the processing system and corresponding to a machine readiness state. Green may signify a “go” state. Red may signify a “no go” state. And yellow may signify a meta-state somewhere in between a “go” and “no go” state, although a yellow and red state are often both treated as a “no go” state.
Referring now to FIG. 1, a simplified diagram of a light tower as is commonly used with plasma processing systems is shown. Light tower status can generally be determined by a process model—usually an “OR” on software and/or hardware alarm thresholds. The light tower itself may be comprised of three discrete color displays, and hence can display three states. A red state 102, generally implies a “no go” machine readiness state, such as a when the plasma process is out of spec. In some configurations, a “no go” machine readiness state will cause the red display to flash. A yellow state 104 generally conveys a warning that the plasma process may soon be approaching a “no go” machine readiness state. A green state 106 generally refers to a “go” state. In general, operators require only a modest knowledge of the process model, unless substantial troubleshooting is required.
However, machine readiness state is somewhat arbitrary and commonly determined from a subset of process parameters that are of particular interest to the plasma processing system owner. Generally, the manufacturing process is stopped only when absolutely necessary, since a single processed substrate can be worth a substantial amount of money. Yet, it is often difficult to achieve a sufficient granularity of the machine process state to make more than what amounts to an educated guess.
For example, pollutants may be cleaned from the plasma processing system by striking the plasma without the substrate. However, since the electrostatic chuck (chuck) is no longer shielded by the substrate, it is subsequently etched. Eventually, the plasma processing system cannot adequately compensate, and the process recipe's parameters are invalidated. Since it is often impractical to determine when this point is exactly reached, the customer may instead determine that a “no go” state is reached, and hence the chuck replaced, after a certain amount of operational hours, which in practice is normally only a fraction of its useful life. This can both increase productions costs, since an expensive chuck may be needless replaced, and reduces yield, since the plasma processing system must be taken offline for several hours to replace the chuck.
In addition, machine readiness may not necessarily correlate to a “go” or “no go” state, but rather to a continuum of expected failure states. An expected failure state is the probability of a particular failure state multiplied by some numerical representation of its impact, damage, or normalized weight. If a failure is highly probable, but its operational impact on the process is negligible, its expected value is small. In this situation, the manufacturing process should probably be continued. Likewise, if a failure is not very probable, but its operational impact on the process is very high, a low expected value may again suggest allowing the process to continue. In contrast, a higher expected failure state may suggest immediately shutting down the machine. For example, a vacuum leak in the plasma chamber.
However, a continuum of expected failure states can be difficult to efficiently present to the operator in a readily comprehensible graphical user display. Although limited, a benefit of a discrete visual notification (i.e., “go” or “no go”) is that it allows the operator to quickly and easily react, such as stopping the plasma processing system. In contrast, a continuum visual notification may often require operators to engage in sophisticated thinking, such as building up mental overviews of the present and future state of affairs in the plasma process, and of the effects of possible actions.
A significant fraction of data analysis models (like regression models, factor analysis, analysis of variances, etc.) tend to be comprehensible to the operator only if the data follows a simple pattern. Yet, it is often difficult to extract raw operational data from a plasma processing system, process that information based on a pre-defined set of algorithms, and subsequently display the processed information in a readily comprehensible graphical display. In addition, operators may need to understand enough about the process and the task to be able to infer the present inner state of a complex multidimensional process from an incomplete graphical display, and to predict its behavior and hence possible failure states.
In view of the foregoing, there are desired computer-implemented data presentation techniques for a plasma processing system.